Article Per Year (5 Year)
p-Index From 2021 - 2026
1.116
P-Index
This Author published in this journals
All Journal Indonesia Berdaya Jurnal Pengabdian Masyarakat (ABDIRA) Prosiding University Research Colloquium Research Horizon Abdimas Mandalika BERBAKTI: Jurnal Pengabdian Kepada Masyarakat
As’ad, Muhammad
Unknown Affiliation
Religion Aerospace Engineering Agriculture, Biological Sciences & Forestry Arts Humanities Computer Science & IT Dentistry Economics, Econometrics & Finance Education Environmental Science Health Professions Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Medicine & Pharmacology Nursing Public Health Social Sciences Veterinary Other

Published : 6 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 1 Documents
Search
Journal : Research Horizon

 1 
Application of Computer Vision for Customer Insights: Demographics and Visit Duration in Coffee Shops Faisal; Rachmat; Saleh, Husna; Irfan; As’ad, Muhammad
Research Horizon Vol. 5 No. 5 (2025): Research Horizon - October 2025
Publisher : LifeSciFi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54518/rh.5.5.2025.811

Abstract

The coffee shop industry is increasingly competitive, requiring business owners to adopt data-driven strategies rather than rely solely on intuition, as traditional approaches such as surveys and manual observation are often subjective, time-consuming, and lack scalability. This study aims to design, implement, and evaluate an end-to-end intelligent system based on computer vision to automatically and non-intrusively analyze customer demographics (age and gender) and visit duration (dwell time). The proposed framework emphasizes both technical accuracy and privacy-by-design principles, where facial data is processed in real time without storage, and only anonymized metadata is utilized for business analysis. Using a simulated 60-minute test video containing 50 virtual customers with balanced gender, varied age groups, and predetermined visit durations, the system was evaluated and demonstrated strong performance, achieving 96% accuracy in gender classification, 89% in age group classification, and a Mean Absolute Error (MAE) of less than 45 seconds in dwell time measurement. The findings confirm that the ethical application of computer vision can provide valuable business insights, including the identification of demographic-based peak hours, the recognition of product preferences, and the optimization of spatial layouts, ultimately enabling coffee shops and SMEs to enhance competitiveness and profitability through data-driven decision-making.
Co-Authors Aan Wilhan Juliatmaja Aini, Hizratun Ainun Jariah Akbar, Fachreza Alfionita, Reka Andi Sahputra Depari Aulia S, Diva Adhiesti Buana, Mahesa Graha Wisesha Cakra Eliya Pebriyeni, S.Pd., M.Sn, HESTI ROSITA DWI PUTRI, Dra. Ernis, M.Pd, Faisal Fatimatuzzahroh, Putri Fuad, M. Noor Garwan, Hellaisna Nur’aini Ghan, Farrel Hasanah, Aan Nurul Hasyim, Rifki Hijriah Hijriah Ibrahim Ibrahim Ismunandar Jumria Jumria Khaerul Anam Mahsup, Mahsup Maulyanti, Restina Mirna Mirna MMSI Irfan ,S. Kom Monika Handayani, Monika Muhammad Ikhwan Mardin Nurcahyani Nurcahyani, Nurcahyani Pebriani, Inez Putri, Dhima Surya Rachmat Saleh, Husna Salsabila, Ilmi Selfi, Selfi Setiawan, Shabian Atha Sintayana Muhardini Situmorang, Raftonado Sudarwo, Raden Susi Susanti Syawal, La Ode Muhamad Irwin Tri Sulistyawati, Tri Yasmin Yasmin